Dynamic 5G network slicing to maximize spectrum utilization

ABSTRACT

In a 5G network, a slice controller operating in a radio access network (RAN) is arranged to make predictions of channel state information (CSI) for user equipment (UE) on the network using a predictive propagation model. The slice controller uses the predicted CSI to schedule subcarriers and time slots associated with physical radio resources for data transmission on slices of the 5G network between a 5G radio unit (RU) and the UE to maximize network throughput on a slice for the radio spectrum that is utilized for a given time period. In view of the CSI predictions, the slice controller controls operations of the MAC (Medium Access Control) layer functions based on PHY (physical) layer radio resource subsets to schedule the subcarrier and time slots for data transmissions on a slice over the 5G air interface from RU to UE.

BACKGROUND

Fifth generation (5G) mobile networks offer the ability to connect tensof billions of intelligent devices, densely deployed and generatingorders of magnitude more data to be handled by the network. Consumers'expectations for 5G mobile networks are high and mobile networkoperators will be under real pressure from enterprise customers to movequickly, delivering 5G's low latency, dense device, and high-performancecapabilities to enable near real-time management and control of criticalbusiness operations.

SUMMARY

In a 5G network, a slice controller operating in a radio access network(RAN) is arranged to make predictions of channel state information (CSI)for user equipment (UE) on the network. The slice controller uses thepredicted CSI to schedule subcarriers and time slots associated withphysical radio resources for data transmission on slices of the 5Gnetwork between a 5G radio unit (RU) and the UE to maximize networkthroughput on a slice for the radio spectrum that is utilized for agiven time period. The slice controller may use a model to make the CSIpredictions. In one illustrative embodiment the model may comprise apredictive propagation model using reinforcement learning that can beupdated using online channel information data or by using a model basedon offline data sources. Using the CSI predictions, the slice controllercontrols operations of the MAC (Medium Access Control) layer functionsbased on PHY (physical) layer radio resource subsets to schedule thesubcarrier and time slots for data transmissions on a slice over the 5Gair interface from RU to UE.

The slice controller advantageously operates to allocate spectrum tomeet dynamic data demands of UE across each of various slices of the 5Gair interface while maximizing throughput and utilization of spectrum,which is a finite commodity. The inventive slice controller uses the CSIpredictions to schedule transmissions using optimal combinations ofsubcarrier and time slots for radio resources from the physicalinfrastructure underlying the 5G network to enable operators and serviceproviders to efficiently and flexibly utilize 5G network capacity tomaximum technical and economic advantage.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter. Furthermore, the claimed subject matter is not limited toimplementations that solve any or all disadvantages noted in any part ofthis disclosure. It will be appreciated that the above-described subjectmatter may be implemented as a computer-controlled apparatus, a computerprocess, a computing system, or as an article of manufacture such as oneor more computer-readable storage media. These and various otherfeatures will be apparent from a reading of the following DetailedDescription and a review of the associated drawings.

DESCRIPTION OF THE DRAWINGS

FIG. 1 shows illustrative 5G network usage scenario footprint examples;

FIG. 2 shows illustrative standardized 5G network slices;

FIG. 3 shows an illustrative layered 5G network slicing framework;

FIG. 4 shows illustrative physical infrastructure in a 5G networkarchitecture;

FIG. 5 shows an illustrative 5G radio access network (RAN) and radiounit (RU);

FIG. 6 shows an illustrative split-RAN hierarchy in which a central unit(CU) may support multiple distributed units (DUs) which, in turn, maysupport multiple RUs;

FIG. 7 shows an illustrative radio resource control (RRC) that isdisaggregated into a mobile core-facing control plane component and anear-real-time RAN intelligent controller (near-RT RIC);

FIG. 8 shows an illustrative RAN operations and maintenance (OAM)logical architecture as described by the O-RAN (Open Radio AccessNetwork) Alliance;

FIG. 9 shows an illustrative 5G implementation in which split-RANfunctional units and instances of the non-real-time RIC (non-RT RIC) maybe distributed among physical infrastructure components;

FIG. 10 shows an illustrative slice controller that may be configured toallocate physical radio resources among RAN slices;

FIG. 11 shows online and offline CSI (channel state information) that isillustratively utilized by a slice controller to generate predicted CSIdata using a predictive propagation model that utilizes reinforcementlearning;

FIG. 12 shows illustrative bandwidth parts (BWP) associated withrespective numerologies;

FIG. 13 shows an illustrative graph depicting the variation inthroughput over time for different subcarriers;

FIG. 14 shows an illustrative slice controller scheduling time slots andsubcarriers for transmissions among slices of an air interface in a 5Gnetwork to maximize throughput between the RU and UE per bandwidth thatis utilized;

FIG. 15 is an illustrative scheduling matrix that shows how datatransmissions for particular UE are scheduled by selecting subcarriersand time slots;

FIGS. 16, 17, and 18 show illustrative methods that may be performedwhen implementing the present dynamic 5G network slicing to maximizespectrum utilization;

FIG. 19 is a block diagram of an illustrative UE that may be used atleast in part to implement the present dynamic 5G network slicing tomaximize spectrum utilization;

FIG. 20 is a block diagram of an illustrative server or computing devicethat may be used at least in part to implement the present dynamic 5Gnetwork slicing to maximize spectrum utilization;

FIG. 21 is a block diagram of an illustrative datacenter that may beused at least in part to implement the present dynamic 5G networkslicing to maximize spectrum utilization; and

FIG. 22 is a simplified block diagram of an illustrative computer systemthat may be used at least in part to implement the present dynamic 5Gnetwork slicing to maximize spectrum utilization.

Like reference numerals indicate like elements in the drawings. Elementsare not drawn to scale unless otherwise indicated.

DETAILED DESCRIPTION

5G mobile networks utilize a service-based architecture that supportsdata connectivity and services enabling deployments using techniquessuch as, for example, Network Function Virtualization (NFV), SoftwareDefined Networking (SDN), and cloud computing. Some exemplary featuresand concepts of 5G networking include separating User Plane (UP)functions from Control Plane (CP) functions allowing independentscalability, evolution, and flexible deployment across, for example,centralized locations and/or distributed (i.e., remote) locations. Thefunctional design of 5G networks is modularized to enable flexible andefficient network slicing. Dependencies are also minimized between theRadio Access Network (RAN) and the Core Network (CN). The 5Garchitecture is thus defined with a converged core network with a commonAN-CN interface which integrates different Access Types, for example3GPP (3rd Generation Partnership Project) access and untrusted non-3GPPaccess such as WiMAX, cdma2000@, WLAN, or fixed networks.

The International Mobile Telecommunications (IMT) recommendation for2020 from the International Telecommunication Union RadiocommunicationSector (ITU-R M.2083-0) envisions usage scenarios for 5G networks thatinclude: Mobile Broadband (MBB), as indicated by reference numeral 105;Ultra-Reliable and Low Latency Communications (URLLC) 110; and MassiveMachine Type Communications (MMTC) 115, as shown in the usage scenarioexamples 100 in FIG. 1 .

The MBB usage scenario 105 addresses the human-centric use cases foraccess to multi-media content, services, and data. The demand for mobilebroadband will continue to increase, leading to enhanced MobileBroadband. The enhanced MBB usage scenario will come with newapplication areas and requirements in addition to existing MBBapplications for improved performance and an increasingly seamless userexperience. The enhanced MBB usage scenario may cover a range of cases,including wide-area coverage and hotspot, which have differentrequirements.

For the hotspot case (i.e., for an area with high user density), veryhigh traffic capacity is needed, while the requirement for mobility istypically low and user data rate is higher than that of wide-areacoverage. For the wide area coverage case, seamless coverage and mediumto high mobility are desired, with much improved user data rate—20 Gbpsfor download and 10 Gbps for upload—compared to existing data rates.However, the data rate requirement may be relaxed compared to hotspot.

The URLLC usage scenario 110 may typically have relatively stringentrequirements for capabilities such as latency and availability. Forexample, latency in the RAN may be expected to be less than 1 ms withhigh reliability. Some examples include wireless control of industrialmanufacturing or production processes, remote medical surgery,distribution automation in a smart grid, transportation safety, etc.

The MMTC usage scenario 115 may be characterized by a very large numberof connected devices such as Internet of Things (IoT) devices withhundreds of thousands of connected devices per square kilometer. MMTCmay also be referred to as “Massive IoT” (MIoT) in some 5G literature.Such connected devices can be expected to transmit a relatively lowvolume of non-delay sensitive data. Devices are typically required to below cost and have a very long battery life.

Illustrative applications for 5G networking are also shown in FIG. 1 .The applications can fall within the usage scenario examples 100 atdifferent locations depending on a given balance of applicationnetworking requirements. As shown, the illustrative applications caninclude three-dimensional and/or ultra-high-definition (3D and UHD) 120;augmented reality 125; industry automation 130; self-driving cars 135;mission-critical infrastructure 140; smart cities 145; voice 150; smarthomes 155; and gigabytes in a second 160.

It is emphasized that the ITU expects additional 5G usage scenarios andapplications to emerge, and 5G network operators may not necessarily belimited to or required to support any particular usage scenarios orpredefined slice types. Similarly, application and service providers maybe expected to leverage the higher speeds and lower latency of 5G todevelop feature-rich capabilities for all kinds of connected devices(both fixed and mobile), deliver compelling user experiences across arange of computing devices and platforms, and further realize thepotential of artificial intelligence (AI) and IoT in a way that currentconnectivity prohibits.

With 5G, mobile networks can be optimized as features such as networkslicing become available for both operators and enterprises deploying 5Ginfrastructure. A network slice is a logical (i.e., virtual) networkcustomized to serve a defined purpose, type/class of service, quality ofservices (QoS), or dedicated customers. A 5G network slice may bedynamically created consisting of an end-to-end composition of all thevaried network resources and infrastructure needed to satisfy thespecific performance requirements of a particular service class orapplication that may meet some predefined service level agreement (SLA).Each portion of the 5G network is respectively sliced such that thenetwork can be viewed as being composed of air interface slices, RANslices, mobile core slices, cloud slices, etc. 5G network slicing thusenables creation of multiple logical and secure networks that areisolated from each other, but which span over the same common physicalnetwork infrastructure.

5G network slices may consist of resources composed into an end-to-endservice delivery construct. These may include physical resources, eithera share or profile allocated to a slice, or dedicated physical resourcesin some cases. Slices also consist of logical entities such asconfigured network functions, management functions, VPNs (virtualprivate networks), etc. Resources (physical or logical) can be dedicatedto a 5G network slice, i.e., separate instances, or they may be sharedacross multiple slices. These resources are not necessarily all producedwithin the mobile network provider as some may comprise servicesconsumed from other providers, facilitating, for example, aggregation,cloud infrastructure, roaming, etc.

3GPP is the principal standards organization engaged in the architecturedevelopment for 5G. Several iterations of standards releases haveestablished a foundation for the current phase of slice-specificdefinition. The 3GPP R15 System Architecture (3GPP TS 23.501) currentlydefines standard service-based Slice/Service types (SST). As shown inFIG. 2 , the standardized 3GPP network slices of a 5G network 205include eMBB (enhanced Mobile Broadband) (SST=1), URLLC (SST=2), andMIoT (SST=3) which correspond to the usage scenarios described by ITU-R2083-0. 3GPP also defines additional standardized SST values for V2X(Vehicle-to-Everything) (SST=4) and HMTC (High-Performance Machine TypeCommunications (SST=5). It may be appreciated that slice service typesbeyond those having standardized SST values can be defined.

The five standardized service types for 5G network slices arerespectively indicated by reference numerals 210, 215, 220, 225, and 230in FIG. 2 . IMT-2020 describes the concept of network slicing assupporting a wide variety of requirements in UE and application servicesusing a network where multiple logical network instances tailored to therequirements can be created. Network slicing allows the 5G networkoperator to provide dedicated logical networks (i.e., network slices)with customer specific functionality. The 5G architecture enablesdifferent network configurations in different network slices.

A network slice can be dedicated to different types of services and spanall the domains of the underlying physical infrastructure 235, such asthe transport network supporting flexible locations of functions,dedicated radio configurations or specific radio access technologies(RATs), and the mobile core network. Network slices can also be deployedacross multiple operators. Slices can share common physicalinfrastructure or may have dedicated resources and/or functions in somecases. Different types of network slices can be composed of not onlystandardized network functions but also some proprietary functions thatmay be provided by different operators or third parties.

Standardized SST values provide a way for establishing globalinteroperability for 5G network slicing so that operators canefficiently support key industry verticals—for example, industrialautomation, healthcare, entertainment, transportation, manufacturing,energy, agriculture, construction, security, etc.—for the most commonlyused Slice/Service Types. Additional customization and/or specializationfor applications and services may be implemented for specific usagescenarios. A UE may provide Network Slice Selection AssistanceInformation (NSSAI) parameters to the network to help it select a RANand a core network part of a slice instance for the device. A singleNSSAI may lead to the selection of several slices. NSSAI consists ofSession Management NSSAIs (SM-NSSAI), which each include an SST andpossibly a Slice Differentiator (SD). SST may refer to an expectednetwork behavior in terms of features, e.g., broadband or IoT, while theSD can help in the selection among several slice instances of the sametype. It is noted that services supported in a standardized slice mayalso be supported by other slices having other (i.e., non-standard) SSTvalues.

FIG. 2 shows user equipment (UE) 200 that may be representative of thewide variety of device types that may utilize 5G networking, including,for example and not by way of limitation, smartphones and computingdevices, drones, robots, process automation equipment, sensors, controldevices, vehicles, transportation equipment, tactile interactionequipment, virtual and augmented reality (VR and AR) devices, industrialmachines, and the like. The standardized slices can be respectivelymapped to such UE types in typical usage scenarios to optimize networkutilization and user experiences, but 5G network slicing is designed forflexibility to meet demand across a wide spectrum of device types anddiverse applications and services. The network softwarization providedby SDN and NFV paradigms in 5G enables network slice configuration—i.e.,how various physical infrastructure and network resources aredeployed—to be rapidly and dynamically adapted to ensure thatperformance objectives are continuously met for 5G applications across agiven population of UEs.

As shown, the configuration of eMBB slice 210 may be optimized forbroadband-everywhere usage scenarios across a wide coverage area forapplications such as consumer entertainment (e.g., video, gaming,streaming), remote offices, etc., where maximized network speeds anddata rates are desired and high traffic volumes are typicallyexperienced. The URLLC slice 215 may be configured for mobilecritical-infrastructure low-latency usage scenarios includingapplications such as remote control operations in medical and industrialenvironments, VR and AR, robotics and automation, etc.

The MIoT slice 220 may be configured for optimal handling of IoT,control, and sensor applications relating to logistics, construction,and metering in vertical industries such as construction andagriculture. The V2X slice 225 may be optimized for automotive andtransportation applications such as telemetry, infotainment, autonomousoperations, enhanced safety, and the like. The HMTC slice 230 istypically configured for optimal handling of non-mobile/fixedcritical-infrastructure applications such as smart factories, smartutilities, etc.

FIG. 3 shows an illustrative layered 5G network slicing framework 300that is described in the IMT-2020 recommendation. The frameworkcomprises a RAN 305, mobile packet core 310, and cloud networkingcomponents 315 that are logically represented in a network sliceinstance layer 320 that sits above a physical infrastructure layer 325in the framework. The physical infrastructure layer provides anabstraction of radio, compute, network, and storage resources which mayinclude, for example, one or more RATs 330, mobile fronthaul (MFH) 335,mobile backhaul (MBH) 340, mobile core network 345, transport 350, andone or more datacenters (DCs) 355. In some cases, one or more UEinstances may be implemented as resources.

In this illustrative example, the slice instance layer includes three 5Gnetwork slices—Slice A 360, Slice B 365, and Slice C 370, but more orfewer slices may be utilized in any given implementation at any giventime. These slices may include one or more of the slice types shown inFIG. 2 and described in the accompanying text or comprise differentslice types.

Slices may be isolated by logically or physically isolating theirunderlying resources. The slices can support instances of variousapplications and/or services (collectively indicated by referencenumeral 375) in a service instance layer 380, for example, using anapplication programming interface (API), as representatively indicatedby reference numeral 385. Each network slice may be viewed as anindependent logical collection of resources which can dynamically varyin configuration from slice to slice as needed to meet predefinedtechnical characteristics (e.g., throughput, latency, reliability, etc.)and/or business characteristics as required by an application/serviceinstance.

A slice controller 390 is utilized with the slicing framework 300 tomaintain awareness of the application requirements to responsivelyallocate and manage the virtualized network functions and resources ineach slice. A service manager and orchestrator 395 combines thenecessary resources and functions to produce a network slice instance.Its main tasks include creation of slice instances upon the underlyingphysical infrastructure, dynamically mapping network functions to sliceinstances to meet changing context, and maintaining communicationbetween the application and services and the framework to manage slicelifecycle.

As shown, a service level agreement (SLA) 398 is typically applicable toeach of the slices 360, 365, and 370. The applicable SLAs can vary inscope and composition. The slice controller 390 may be advantageouslyutilized to perform resource allocation among RAN slices to meet theconnectivity requirements while ensuring compliance with applicable SLAguarantees in some implementations.

An SLA may be defined as a contract between the provider of a serviceand its internal or external end-user or customer that defines whatservices the provider will offer and the level of performance it mustmeet as well as any remedies or penalties should the agreed-upon levelsnot be realized. According to the ITU, an “SLA is a formal agreementbetween two or more entities that is reached after a negotiatingactivity with the scope to assess service characteristics,responsibilities and priorities of every part.” SLAs typically establishcustomer expectations for a provider's performance and quality. Varioustypes of customers can be supported by the present dynamic 5G networkslicing methodologies, typically depending on applicable circumstancesand context. For example, customers may include, but are not limited toconsumers, businesses, enterprises, organizations, service providers,application developers, and the like. A 5G network operator may supportits own services to customers as well as services from multipledifferent third-party providers. For example, one third-party providermay offer services to customers on one particular network slice whileanother third-party provider offers services on another network slice.Each discrete service offering may have its own corresponding distinctSLA.

SLA terms may include metrics covering technical aspects of service, forexample describing a level and volume of communication services andwhich measure the performance characteristics of a provided service.Such technical metrics may include but not be limited to, for example,availability, throughput, latency, bit/packet error rate, and energy.SLAs may also include business, economic, and legal terms covering theagreement between the service provider and the customer. SLAs fordifferent service and slice types can vary. For example, some slicetypes have more elasticity with regard to RAN resource allocation whereresources can be readily adjusted depending on resource demand. Otherslice types may be more inelastic. For example, the URLLC slice type mayrequire strict resource allocation to guarantee reliability and lowlatency under a corresponding SLA, while enhanced MBB resources may bereadily scaled downward once the edge cloud buffering is complete.

FIG. 4 shows illustrative physical infrastructure in a 5G networkarchitecture 400. Multiple instances of a radio unit (RU) 405 areconfigured to interact with a diverse population of UE 200. Each UEtypically includes one or more local applications 410 or client-sidesoftware/firmware component that is arranged to interface with one ormore remote application servers, service providers, or other resources(collectively indicated by reference numeral 415) and thus requirenetwork connectivity to such remote facilities.

The RUs are coupled by the mobile fronthaul 335 to a RAN 420. The RAN iscoupled by the mobile backhaul 340 to one or more datacenters (DCs). Inthis illustrative example, the DCs comprise an edge DC 425, a metro DC430, and a central DC 435. In some 5G networking literature, the edge DCmay be referred to as a far edge or on-premises DC. The metro DC may bereferred to as a near edge DC, and the central DC may be referred to asthe cloud. In some implementations, the edge DC may support multi-accessedge computing (MEC) functions 440.

The application servers 415 can be located at various points in thenetwork architecture 400 to meet technical requirements and trafficdemands. Typically, the application servers will be physically locatedcloser to the UE 200 in cases where latency is sought to be minimized.However, an operator's application server location criteria may alsoconsider factors such as management ease, scalability, and security,among other factors. In some implementations, an operator may optionallydeploy application servers and other resources in the RAN 420 or RU 405,as indicated by the dashed circles in FIG. 4 .

FIG. 5 shows functional blocks of the RAN 420 and RU 405. The RUcomprises radio transmission points, for example, a next generation NodeB, gNB 505, which handles radio communications with the UE. The gNB isserially coupled to a radio frequency (RF) front end 510, a digital toanalog (D/A) conversion unit 515, and a portion of the functionality ofthe physical (PHY) layer 520 as described in the OSI Open SystemsInterconnection model.

Under 3GPP and O-RAN (Open RAN) Alliance, the processing pipeline of theRAN 420 is split into a distributed unit (DU) 525, and a central unit(CU) 530. The DU is responsible for real-time layers 1 and 2 (L1 and L2)scheduling functions, and the CU is responsible for non-real-time,higher L2 and L3 functions. Accordingly, the DU comprises a scheduler535 located on top of a MAC (Medium Access Control) layer component 540,an RLC (radio link control) layer component 545, and parts of a PHY(physical) layer component 520. The MAC layer component is responsiblefor buffering, multiplexing and demultiplexing segments, including allreal-time scheduling decisions regarding which segments are transmittedwhen. It is also able to make a “late” forwarding decision (i.e., toalternative carrier frequencies, including Wi-Fi, for example). The PHYlayer component is responsible for coding and modulation.

The CU 530 is configured with a PDCP (Packet Data Convergence Protocol)layer component 550 and RRC (Radio Resource Control) layer component555. The PDCP layer component is responsible for compressing anddecompressing IP headers, ciphering and integrity protection, and makingan “early” forwarding decision (i.e., whether to send the packet downthe pipeline to the UE or forward it to another base station). The RRClayer component is responsible for configuring the coarse-grain andpolicy-related aspects of the RAN processing pipeline. The RRC layercomponent interfaces with the mobile core control plane while the PDCPlayer component interfaces with the user plane to thereby implement the“CUPS” (control and user plane separation) feature of 5G.

The split-RAN configuration shown in FIG. 5 enables RAN functionality tobe split among physical infrastructure elements in centralized anddistributed locations. For example, as shown in FIG. 6 , a single CU 530may be configured to serve multiple DUs 525, each of which in turnserves multiple RUs 405.

FIG. 7 shows that the RRC layer component 555 may be disaggregated intoa mobile core-facing control plane forwarding component 705 and anear-real-time (RT) controller RAN intelligent controller (RIC) 710. TheRRC layer component is thus responsible for only near-real-timeconfiguration and control decision making, while the scheduler 535 onthe MAC component 540 is responsible for real-time scheduling decisions.

FIG. 8 shows an illustrative RAN operations and maintenance (OAM)logical architecture 800, as described by the O-RAN Alliance. In thedrawing, the “O” prefix indicates the O-RAN implementation for thefunctional elements of the architecture. The O-RAN Alliance defines andmaintains the A1, E2, O1, O2, and Open Fronthaul interfaces discussedbelow. As shown, a non-RT RIC 805 may be incorporated into the servicemanager and orchestrator 395. The non-RT RIC interoperates with anear-RT RIC 710 through an A1 interface 810.

The near-RT RIC 710 is coupled over an E2 interface 815 with networkfunctions for radio access control and optimization including theO-CU-CP (O-RAN Central Unit-Control Plane) 820, O-CU-UP (O-RAN CentralUnit-User Plane) 825, and O-DU 830. The O-CU-CP and O-CU-UP arerespectively coupled to the O-DU over F1-c and F1-u interfaces, 840 and845, as defined and maintained by 3GPP. The O-CU-CP is coupled to theO-CU-UP over a 3GPP E1 interface 850. The O-DU and O-RU 835 are coupledusing an Open Fronthaul interface 855 (also known as a lower layer split(LLS) interface).

The O-Cloud 860 is a cloud computing platform comprising a collection ofphysical infrastructure nodes that meet O-RAN requirements to host therelevant O-RAN functions (i.e., near-RT RIC, O-CU-CP, O-CU-UP, andO-DU), the supporting software components (such as Operating System,Virtual Machine Monitor, Container Runtime, etc.), and the appropriatemanagement and orchestration functions to create virtual networkinstances and map network functions. The O-Cloud is coupled to theservice manager and orchestrator 395 over the O2 interface 865. An O1interface 870 is provided to each of the near-RT RIC, O-CU-CP, O-CU-UP,O-DU, and O-RU, as shown in FIG. 8 .

The splitting of functional elements among the DU, CU, near-RT RIC, andnon-RT RIC, as discussed above, enables flexible deployment of instancesof such elements in the physical infrastructure that underlies a typical5G network. FIG. 9 shows an illustrative 5G implementation in whichsplit-RAN functional units and instances of the non-RT RIC may bedistributed among physical infrastructure components. For example, asshown, a DU 525 and CU 530 may be located at the edge DC 425. A CU 530and non-RT RIC 805 may be located in the metro DC 430. The central DC435 can also host a non-RT RIC in some cases.

FIG. 10 shows the slice controller 390 as illustratively configured toallocate physical radio resources among network slices. The slicecontroller may be instantiated, for example, as a component of thenear-RT RIC 710 (FIG. 7 ) to thereby implement or combine thefunctionality of the scheduler 535 (FIG. 5 ). In alternativeimplementations, part of the slice controller may be distributed outsidethe near-RT RIC, for example, in a CU in an edge or metro DC, orincluded in one or more other functional elements of the 5G networkarchitecture. In some implementations, dynamically optimized RAN actionsto ensure SLA guarantees may be performed in the near-RT RIC whilelonger term SLA assurance can be handled in the non-RT RIC.

The slice controller 390 is arranged to control operations of the MACcomponent 540 based on logical representations 1005 of a radio resourcein the PHY component 520 (FIG. 5 ). As shown, the MAC component 540performs intra-slice resource allocation. More specifically, thephysical radio resource 1010 is partitioned into multiple blocks orsegments each defined by one numerology to meet certain communicationrequirements, such as low latency, wide coverage, etc. Numerology refersto the values of the basic physical transmission parameters,particularly including, for example, the transmission time slot lengthin which length of the slot is shorter for higher numerologies.

Each RAN portion of a network slice occupies a subset of physicalresources taken from one or multiple numerology segments which may berepresented, as shown in FIG. 10 , using dimensions comprising frequencyand time. In 5G, frame structures of the radio resources in the timedomain are 10 ms in length irrespective of the numerology in use but mayvary in width in the frequency domain. For example, a RAN slice servingautomotive services in a high mobility scenario may use a widersubcarrier spacing to combat high Doppler shifts, while a RAN sliceserving a latency-sensitive service such as real-time gaming may usefewer symbols in each sub-frame. It may be appreciated that spatialmultiplexing, referred to as MIMO (multiple input, multiple output), mayalso be utilized to provide additional layers of RAN resources that theslice controller may allocate in some implementations.

As illustratively shown in FIG. 11 , the slice controller 390 may beconfigured to utilize channel state information (CSI) that it collectsfrom online sources 1140 of channel information data to update apredictive propagation model 1110 that may implement reinforcementlearning (indicated by reference numeral 1105). Alternatively, apredictive propagation model can be based on offline CSI data sources1150. CSI parameters are the quantities related to the state of a 5Gradio channel that are reported by the UE 200 to a gNB 505 as feedback1125. The CSI feedback includes several parameters to report dynamicchannel conditions between the UE and gNB, such as the CQI (channelquality indicator), the PMI (precoding matrix indicator) with differentcodebook sets, and the rank indicator (RI). The channel stateinformation reference signal (CSI-RS) 1130 is used to measure the CSIfeedback.

The CSI-RS is transmitted by the gNB as a known reference signal whichthe UE measures and then reports the radio channel properties back tothe gNB. Channel conditions are typically reasonably stable withcompletely stationary UE. With limited changes in multipath propagation,most channel variations should come from interference from other cellsor UE. However, mobile UE may experience vastly different and rapidlychanging radio conditions, especially as they may move in and out of aline of sight to the gNB. When the gNB receives the CSI parameters fromthe UE, it can schedule the downlink data transmissions 1135 (such asmodulation scheme, code rate, number of transmission layers, and MIMOprecoding) accordingly.

The CSI data can be collected online at the slice controller 390 asnear-real-time data 1140, for example from the O-DU 830 (FIG. 8 ) overthe E2 interface 815 and used in the predictive propagation model 1110to generate CSI predictions 1145. These predictions are utilized by theslice controller to determine RAN admission in response to an admissionrequest from an application/UE, as discussed below. Alternatively, apredictive propagation model can be based on CSI data from offlinesources 1150. It may be appreciated that various combinations of onlineand/or offline CSI data, learning methods, algorithms, and/or predictivemodels may be utilized to make CSI predictions to meet the needs ofparticular implementations of 5G network admission. In addition, machinelearning and/or artificial intelligence may also be utilized for the CSIpredictions in some implementations.

Each numerology may have a defined bandwidth part (BWP) that can havevarious parameters including subcarrier spacing, OFDM (orthogonalfrequency-division multiplexing) symbol duration, and cyclic prefix (CP)length. A BWP is a contiguous set of physical resource blocks (RBs) fora given carrier. The RBs are selected from a contiguous subset of commonRBs for a given numerology, μ. FIG. 12 shows illustrative BWPs 1205,1210, and 1215 associated with respective numerologies, μ=0, 1, and 2.Frequency is shown on the horizontal axis and time is shown on thevertical axis.

It may be appreciated that a wider bandwidth may have a direct impact onthe peak and user experienced data rates, however users are not alwaysdemanding high data rates. The use of wide bandwidth may imply higheridling power consumption both from RF and baseband signal processingperspectives in some cases. Thus, the concept of BWP has been introducedin 5G to thereby operate UEs with smaller bandwidth than the configuredchannel bandwidth, which enables the 5G air interface to be efficientwhile still supporting wideband operations. BWPs provide flexibility sothat multiple, different signal types can be sent in a given bandwidth.Most gNBs can utilize the wider bandwidths available in 5G. UEcapabilities, however, can be expected to vary and it will be morechallenging for some UEs to use the larger available bandwidths. BWPsenable multiplexing of different signals and signal types for betterutilization of spectrum.

Per 3GPP release 15, a given UE can be configured with a maximum of fourBWPs for downlink and uplink but at a given point in time only one BWPis active for downlink and one for uplink. The BWP concept enables UEsto operate in narrow bandwidth and when a user demands more data (e.g.,for bursts of traffic) it can inform the RU to enable wider bandwidth.In typical situations, UEs are expected to receive and transmit onlywithin the frequency range configured for the active BWPs with theassociated numerologies.

As shown in FIG. 12 , each BWP 1205, 1210, and 1215 has a differentsubcarrier spacing. In 5G, subcarrier spacings of 15 kHz (μ=0), 30 kHz(μ=1), 60 kHz (μ=2), 120 kHz (μ=3), and 240 kHz (μ=4) are supported.Downlink and uplink transmissions are organized into frames with 10 msduration, each consisting of ten subframes of 1 ms. Each frame isdivided into two equally-sized half-frames of five subframes each withhalf-frame 0 consisting of subframes 0-4 and half-frame 1 consisting ofsubframes 5-9. In Total, there are 10 subframes in one frame.

The lengths of the respective time slots 1220, 1225, and 1230 aredifferent depending on different subcarrier spacing with slot lengthgetting shorter as subcarrier spacing gets wider. The number of timeslots per subframe varies with carrier spacing—with 1, 2, 4, 8, or 16slots per subframe. The physical radio resources 1010 (FIG. 10 ) aredepicted in FIG. 12 by the filled rectangles which represent thesmallest unit of resource—the resource element 1235—which comprises asingle OFDM symbol and a single subcarrier.

The utilization of mixed numerologies in 5G provides additionalflexibility to efficiently serve requirements of diverse usagescenarios. However, inter-numerology interference (INI) can arisebetween multiplexed numerologies. INI can cause, for example, a loss oforthogonality among subcarriers of different numerologies in thefrequency domain and may cause difficulties in OFDM symbol alignment inthe time domain. Controlling and reducing INI may typically be performedusing one or more techniques including, for example, windowing andfiltering. Guard bands (representatively indicated by reference numeral1240 in FIG. 12 ) may also be inserted between adjacent sub-bandsutilizing different numerologies to minimize the effects of IN in thesystem.

FIG. 13 shows an illustrative graph 1300 depicting the variation inthroughput over time for three different subcarriers 1, 2, N. Throughputcan vary in the wireless 5G environment for various reasons such asreceived signal strength attenuation between transmitter and receiver,signal reflection and scattering, interference, and multipath fading.The net effect of these variations of the wireless channel is a lowersignal-to-noise ratio, which leads to a high error rate and a reductionin the effective data rate, or throughput, between the RU and UE.Accordingly, 5G networks require special strategies to combat fading atthe PHY layer, and intelligent scheduling schemes to provide radioresource allocation that maximizes spectrum utilization and throughput.

FIG. 14 shows the slice controller 390 scheduling RAN radio resources1010 among slices 1405, 1410, and 1415 of the 5G air interface between apopulation 1420 of UE 200 and RU 405 in accordance with principles ofthe present invention. The scheduling comprises dynamically selectingparticular subcarriers and time slots for use in transmitting queueddata traffic (not shown) between the RU and UE. The slice controlleruses the CSI predictions 1145 to deterministically select the particularsubcarriers and time slots to schedule the data traffic for transmissionby the PHY layer 520 (FIG. 5 ).

The scheduling techniques employed by the slice controller 390 aredesigned to maximize total throughput over the 5G air interface betweenthe RU and UE for a given slice for which the RAN resources in the PHYlayer are allocated per bandwidth utilized. It may be appreciated thatINI may be managed by utilization of suitable guard bands betweenslices. The slice controller thus provides more optimal scheduling tomaximize spectrum utilization compared to conventional schedulingalgorithms, such as round robin, that do not maintain awareness ofchannel conditions and which may be configured to emphasize otherperformance metrics. In addition, the present scheduling techniquesprovide further technical advantages by using the predicted (i.e.,future) channel conditions which enables more accurate optimization ofspectrum utilization. As all schedulers need to work ahead of time inthe MAC layer to allocate time slot x+d (where d is a configurable delaytime for processing) while the PHY layer is transmitting a particulartime slot x over the air, the utilization of a prediction of a futureevent more closely aligns future transmissions with appropriate channelconditions to maximize throughput per slice for the given bandwidthutilized.

FIG. 15 is an illustrative scheduling matrix 1500 that shows how datatransmissions for particular UE are scheduled by the slice controller byselecting subcarriers and time slots for a given slice. Bandwidth isdisplayed on the vertical axis and time is displayed on the horizontalaxis. Each square in the matrix represents an arbitrarily-representedunit of physical radio resource that is defined by a single subcarrierand a single time slot. It will be appreciated however that thepresentation in FIG. 15 is chosen to aid clarity in exposition of thepresent principles and that the scheduling matrix could also berepresented using different dimensions. In addition, the matrix showstwo UE, but it will be appreciated that the scheduling may be performedfor more than two in typical implementations.

As the PHY layer 520 (FIG. 5 ) is transmitting the current time slot(i.e., located in the first column of the matrix 1500), the slicecontroller is selecting a particular subcarrier and time slot for futuretransmissions of data that are queued for handling. As the slicecontroller is optimizing scheduling for maximum throughput and spectrumutilization for a slice based on the predicted channel conditions fromthe CSI predictions, not every available subcarrier and time slot in thematrix is necessarily utilized. Subcarriers may be selected across BWPand numerologies in some cases.

FIG. 16 is a flowchart of an illustrative method 1600 that may beperformed in a 5G network for scheduling data transmissions on an airinterface that is established between an RU and a plurality of UE inwhich the 5G network comprises a plurality of slices. The method 1600may be performed, for example, by the slice controller 390 (FIG. 1 ) orby another suitable component or functionality disposed in the near-RTRIC 710 (FIG. 7 ). Unless specifically stated, methods or steps shown inthe flowchart blocks and described in the accompanying text are notconstrained to a particular order or sequence. In addition, some of themethods or steps thereof can occur or be performed concurrently and notall the methods or steps have to be performed in a given implementationdepending on the requirements of such implementation and some methods orsteps may be optionally utilized.

At block 1605, physical radio resources are allocated in the airinterface, in which the physical radio resources are partitioned insegments comprising respective subcarriers and time slots, and in whichthe subcarriers use dimensions of bandwidth and the time slots usedimensions of time. At block 1610, CSI data is collected from the UEthat represents conditions for data transmission over the air interfacebetween the RU and UE. At block 1615, the collected CSI data is used topredict CSI that is applicable for subcarriers at future time slots thatare available for handling the data transmissions. At block 1620, thepredicted CSI is used to select subcarriers and time slots for datatransmissions over the air interface between the RU and the UE for aslice of the 5G network, in which criteria for the selection comprisemaximization of throughput for the slice over the air interface perbandwidth utilized, in which the throughput comprises data transmittedper unit time.

FIG. 17 is a flowchart of an illustrative method 1700 that may beperformed in a 5G network. For example, the method 1700 may be performedby the slice controller 390 (FIG. 1 ) or by another suitable componentor functionality disposed in the near-RT RIC 710 (FIG. 7 ). At block1705, a CSI prediction model is provided to predict CSI for UEconnecting to a 5G network over an air interface that is establishedbetween the UE and an RU, in which the 5G network comprises a pluralityof slices. At block 1710, the predicted CSI is used to identify physicalradio resources to allocate to a slice among the plurality to transmitdata from the RU to the UE, in which the physical radio resourcescomprise a combination of a radio channel subcarrier selected from amonga plurality of different subcarriers and a time slot in radio framesselected from among a plurality of different time slots. At block 1715,data transmissions are scheduled for the slice using the identifiedphysical radio resources based on the predicted CSI to maximizethroughput for the slice over the air interface.

FIG. 18 is a flowchart of an illustrative method 1800 that may beperformed in a 5G network. For example, the method 1800 may be performedby the slice controller 390 (FIG. 1 ) or by another suitable componentor functionality disposed in the near-RT RIC 710 (FIG. 7 ). At block1805, a RAN in a 5G network is dynamically operated by collecting CSIdata in response to changing conditions on the 5G network applicable tocurrent data transmissions over slices of an air interface establishedbetween UE and an RU in the 5G network, in which the 5G networkcomprises a plurality of slices that are respectively associated withthe slices of the air interface. At block 1810, the collected CSI datais used to generate predicted CSI for future data transmissions over theslices of the air interface.

At block 1815, a combination of subcarrier and time slot is selected foreach of the future data transmissions using the predicted CSI. At block1820, the future data transmissions are scheduled on the slices of theair interface using the selected combination of subcarrier and timeslot.

FIG. 19 is a block diagram of an illustrative UE 200 that may be used atleast in part to implement the present dynamic 5G network slicing tomaximize spectrum utilization. The embodiment of the UE 200 shown inFIG. 19 is for illustration only, and the UEs 200 shown in the drawingsand described in the preceding text may have the same or similarconfiguration. However, it is noted that UEs may come in a wide varietyof configurations, and FIG. 19 does not limit the scope of the presentdisclosure to any particular implementation of a UE.

The UE 200 includes an antenna 1910, a radio frequency (RF) transceiver1915, transmit (TX) processing circuitry 1920, a microphone 1925, andreceive (RX) processing circuitry 1930. The UE 200 also includes aspeaker 1935, a processor 1940, an input/output (I/O) interface 1945, aninput device 1950, a display 1955, and a memory 1960. The memoryincludes an operating system (OS) program 1965 and one or moreapplications 410.

The RF transceiver 1915 receives from the antenna 1910, an incoming RFsignal transmitted by a gNB of a 5G network 400 (FIG. 4 ). The RFtransceiver down-converts the incoming RF signal to generate anintermediate frequency (IF) or baseband signal. The IF or basebandsignal is sent to the RX processing circuitry 1930, which generates aprocessed baseband signal by filtering, decoding, and/or digitizing thebaseband or IF signal. The RX processing circuitry transmits theprocessed baseband signal to the speaker 1935 (such as for voice data)or to the processor 1940 for further processing (such as for webbrowsing data).

The TX processing circuitry 1920 receives analog or digital voice datafrom the microphone 1925 or other outgoing baseband data (such as webdata, e-mail, or interactive video game data) from the processor 1940.The TX processing circuitry 1920 encodes, multiplexes, and/or digitizesthe outgoing baseband data to generate a processed baseband or IFsignal. The RF transceiver 1915 receives the outgoing processed basebandor IF signal from the TX processing circuitry and up-converts thebaseband or IF signal to an RF signal that is transmitted via theantenna.

The processor 1940 can include one or more processors or otherprocessing devices and execute the OS program 1965 stored in the memory1960 to control the overall operation of the UE 200. For example, theprocessor may control the reception of forward channel signals and thetransmission of reverse channel signals by the RF transceiver 1915, theRX processing circuitry 1930, and the TX processing circuitry 1920 inaccordance with well-known principles. In some embodiments, theprocessor 1940 includes at least one microprocessor or microcontroller.

The processor 1940 may be configured for executing other processes andprograms resident in the memory 1960, such as operations for CSImeasurement and reporting for systems described in embodiments of thepresent disclosure. The processor can move data into or out of thememory as required by an executing process. In some embodiments, theprocessor may be configured to execute the applications 405 based on theOS program 1965 or in response to signals received from gNBs or anoperator. The processor is also coupled to the I/O interface 1945, whichprovides the UE 200 with the ability to connect to other computingdevices such as laptop computers and handheld computers. The I/Ointerface may thus function as a communication path between suchaccessories and the processor.

The processor 1940 is also coupled to the input device 1950 (e.g.,keypad, touchscreen, buttons etc.) and the display 1955. A user of theUE 200 can typically employ the input device to enter data into the UE.For example, the display can be a liquid crystal display or otherdisplay capable of rendering text and/or graphics, video, etc., from websites, applications and/or service providers.

The memory 1960 is coupled to the processor 1940. Part of the memory mayinclude a random access memory (RAM), and another part of the memory mayinclude a Flash memory or other read-only memory (ROM).

As described in more detail below, the UE 200 can perform signaling andcalculation for CSI reporting. Although FIG. 19 shows one illustrativeexample of UE 200, it may be appreciated that various changes may bemade to the drawing. For example, various components may be combined,further subdivided, or omitted and additional components may be addedaccording to particular needs. As a particular example, the processor1940 may be divided into multiple processors, such as one or morecentral processing units (CPUs) and one or more graphics processingunits (GPUs). Also, while FIG. 19 depicts the UE 200 as configured as amobile device, such as a smartphone, UEs may be configured to operate asother types of portable or stationary devices.

FIG. 20 shows an illustrative architecture 2000 for a computing device,such as a server, capable of executing the various components describedherein for 5G admission by verifying slice SLA guarantees. Thearchitecture 2000 illustrated in FIG. 20 includes one or more processors2002 (e.g., central processing unit, dedicated AI chip, graphicsprocessing unit, etc.), a system memory 2004, including RAM (randomaccess memory) 2006 and ROM (read only memory) 2008, and a system bus2010 that operatively and functionally couples the components in thearchitecture 2000. A basic input/output system containing the basicroutines that help to transfer information between elements within thearchitecture 2000, such as during startup, is typically stored in theROM 2008. The architecture 2000 further includes a mass storage device2012 for storing software code or other computer-executed code that isutilized to implement applications, the file system, and the operatingsystem. The mass storage device 2012 is connected to the processor 2002through a mass storage controller (not shown) connected to the bus 2010.The mass storage device 2012 and its associated computer-readablestorage media provide non-volatile storage for the architecture 2000.Although the description of computer-readable storage media containedherein refers to a mass storage device, such as a hard disk or CD-ROMdrive, it may be appreciated by those skilled in the art thatcomputer-readable storage media can be any available storage media thatcan be accessed by the architecture 2000.

By way of example, and not limitation, computer-readable storage mediamay include volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer-readable instructions, data structures, program modules, orother data. For example, computer-readable media includes, but is notlimited to, RAM, ROM, EPROM (erasable programmable read only memory),EEPROM (electrically erasable programmable read only memory), Flashmemory or other solid state memory technology, CD-ROM, DVDs, HD-DVD(High Definition DVD), Blu-ray, or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by the architecture 2000.

According to various embodiments, the architecture 2000 may operate in anetworked environment using logical connections to remote computersthrough a network. The architecture 2000 may connect to the networkthrough a network interface unit 2016 connected to the bus 2010. It maybe appreciated that the network interface unit 2016 also may be utilizedto connect to other types of networks and remote computer systems. Thearchitecture 2000 also may include an input/output controller 2018 forreceiving and processing input from a number of other devices, includinga keyboard, mouse, touchpad, touchscreen, control devices such asbuttons and switches or electronic stylus (not shown in FIG. 20 ).Similarly, the input/output controller 2018 may provide output to adisplay screen, user interface, a printer, or other type of outputdevice (also not shown in FIG. 20 ).

It may be appreciated that the software components described herein may,when loaded into the processor 2002 and executed, transform theprocessor 2002 and the overall architecture 2000 from a general-purposecomputing system into a special-purpose computing system customized tofacilitate the functionality presented herein. The processor 2002 may beconstructed from any number of transistors or other discrete circuitelements, which may individually or collectively assume any number ofstates. More specifically, the processor 2002 may operate as afinite-state machine, in response to executable instructions containedwithin the software modules disclosed herein. These computer-executableinstructions may transform the processor 2002 by specifying how theprocessor 2002 transitions between states, thereby transforming thetransistors or other discrete hardware elements constituting theprocessor 2002.

Encoding the software modules presented herein also may transform thephysical structure of the computer-readable storage media presentedherein. The specific transformation of physical structure may depend onvarious factors, in different implementations of this description.Examples of such factors may include, but are not limited to, thetechnology used to implement the computer-readable storage media,whether the computer-readable storage media is characterized as primaryor secondary storage, and the like. For example, if thecomputer-readable storage media is implemented as semiconductor-basedmemory, the software disclosed herein may be encoded on thecomputer-readable storage media by transforming the physical state ofthe semiconductor memory. For example, the software may transform thestate of transistors, capacitors, or other discrete circuit elementsconstituting the semiconductor memory. The software also may transformthe physical state of such components in order to store data thereupon.

As another example, the computer-readable storage media disclosed hereinmay be implemented using magnetic or optical technology. In suchimplementations, the software presented herein may transform thephysical state of magnetic or optical media, when the software isencoded therein. These transformations may include altering the magneticcharacteristics of particular locations within given magnetic media.These transformations also may include altering the physical features orcharacteristics of particular locations within given optical media tochange the optical characteristics of those locations. Othertransformations of physical media are possible without departing fromthe scope and spirit of the present description, with the foregoingexamples provided only to facilitate this discussion.

In light of the above, it may be appreciated that many types of physicaltransformations take place in the architecture 2000 in order to storeand execute the software components presented herein. It also may beappreciated that the architecture 2000 may include other types ofcomputing devices, including wearable devices, handheld computers,embedded computer systems, smartphones, PDAs, and other types ofcomputing devices known to those skilled in the art. It is alsocontemplated that the architecture 2000 may not include all of thecomponents shown in FIG. 20 , may include other components that are notexplicitly shown in FIG. 20 , or may utilize an architecture completelydifferent from that shown in FIG. 20 .

FIG. 21 is a high-level block diagram of an illustrative datacenter 2100that provides cloud computing services or distributed computing servicesthat may be used to implement the present dynamic 5G network slicing tomaximize spectrum utilization. Datacenter 2100 may incorporate one ormore of the features disclosed in the DCs shown in the drawings anddisclosed in the accompanying text. A plurality of servers 2101 aremanaged by datacenter management controller 2102. Load balancer 2103distributes requests and computing workloads over servers 2101 to avoida situation wherein a single server may become overwhelmed. Loadbalancer 2103 maximizes available capacity and performance of theresources in datacenter 2100. Routers/switches 2104 support data trafficbetween servers 2101 and between datacenter 2100 and external resourcesand users (not shown) via an external network 2105, which may be, forexample, a local area network (LAN) or the Internet.

Servers 2101 may be standalone computing devices, and/or they may beconfigured as individual blades in a rack of one or more server devices.Servers 2101 have an input/output (I/O) connector 2106 that managescommunication with other database entities. One or more host processors2107 on each server 2101 run a host operating system (O/S) 2108 thatsupports multiple virtual machines (VM) 2109. Each VM 2109 may run itsown O/S so that each VM O/S 2110 on a server is different, or the same,or a mix of both. The VM O/Ss 2110 may be, for example, differentversions of the same O/S (e.g., different VMs running different currentand legacy versions of the Windows® operating system). In addition, oralternatively, the VM O/Ss 2110 may be provided by differentmanufacturers (e.g., some VMs running the Windows® operating system,while other VMs are running the Linux® operating system). Each VM 2109may also run one or more applications (App) 2111. Each server 2101 alsoincludes storage 2112 (e.g., hard disk drives (HDD)) and memory 2113(e.g., RAM) that can be accessed and used by the host processors 2107and VMs 2109 for storing software code, data, etc. In one embodiment, aVM 2109 may employ the data plane APIs as disclosed herein.

Datacenter 2100 provides pooled resources on which customers or tenantscan dynamically provision and scale applications as needed withouthaving to add servers or additional networking. This allows tenants toobtain the computing resources they need without having to procure,provision, and manage infrastructure on a per-application, ad-hoc basis.A cloud computing datacenter 2100 allows tenants to scale up or scaledown resources dynamically to meet the current needs of their business.Additionally, a datacenter operator can provide usage-based services totenants so that they pay for only the resources they use, when they needto use them. For example, a tenant may initially use one VM 2109 onserver 2101 ₁ to run their applications 2111. When demand for anapplication 2111 increases, the datacenter 2100 may activate additionalVMs 2109 on the same server 2101 ₁ and/or on a new server 2101 _(N) asneeded. These additional VMs 2109 can be deactivated if demand for theapplication later drops.

Datacenter 2100 may offer guaranteed availability, disaster recovery,and back-up services. For example, the datacenter may designate one VM2109 on server 2101 ₁ as the primary location for the tenant'sapplication and may activate a second VM 2109 on the same or a differentserver as a standby or back-up in case the first VM or server 2101 ₁fails. The datacenter management controller 2102 automatically shiftsincoming user requests from the primary VM to the back-up VM withoutrequiring tenant intervention. Although datacenter 2100 is illustratedas a single location, it will be understood that servers 2101 may bedistributed to multiple locations across the globe to provide additionalredundancy and disaster recovery capabilities. Additionally, datacenter2100 may be an on-premises, private system that provides services to asingle enterprise user or may be a publicly accessible, distributedsystem that provides services to multiple, unrelated customers andtenants or may be a combination of both.

Domain Name System (DNS) server 2114 resolves domain and host names intoIP addresses for all roles, applications, and services in datacenter2100. DNS log 2115 maintains a record of which domain names have beenresolved by role. It will be understood that DNS is used herein as anexample and that other name resolution services and domain name loggingservices may be used to identify dependencies, for example, in otherembodiments, IP or packet sniffing, code instrumentation, or codetracing.

Datacenter health monitoring 2116 monitors the health of the physicalsystems, software, and environment in datacenter 2100. Health monitoring2116 provides feedback to datacenter managers when problems are detectedwith servers, blades, processors, or applications in datacenter 2100 orwhen network bandwidth or communications issues arise.

Access control service 2117 determines whether users are allowed toaccess particular connections and services provided at the datacenter2100. Directory and identity management service 2118 authenticates usercredentials for tenants on datacenter 2100.

FIG. 22 is a simplified block diagram of an illustrative computer system2200 such as a PC, client machine, or server with which the presentdynamic 5G network slicing to maximize spectrum utilization may beimplemented. Computer system 2200 includes a processor 2205, a systemmemory 2211, and a system bus 2214 that couples various systemcomponents including the system memory 2211 to the processor 2205. Thesystem bus 2214 may be any of several types of bus structures includinga memory bus or memory controller, a peripheral bus, or a local bususing any of a variety of bus architectures. The system memory 2211includes read only memory (ROM) 2217 and random access memory (RAM)2221. A basic input/output system (BIOS) 2225, containing the basicroutines that help to transfer information between elements within thecomputer system 2200, such as during startup, is stored in ROM 2217. Thecomputer system 2200 may further include a hard disk drive 2228 forreading from and writing to an internally disposed hard disk (notshown), a magnetic disk drive 2230 for reading from or writing to aremovable magnetic disk 2233 (e.g., a floppy disk), and an optical diskdrive 2238 for reading from or writing to a removable optical disk 2243such as a CD (compact disc), DVD (digital versatile disc), or otheroptical media. The hard disk drive 2228, magnetic disk drive 2230, andoptical disk drive 2238 are connected to the system bus 2214 by a harddisk drive interface 2246, a magnetic disk drive interface 2249, and anoptical drive interface 2252, respectively. The drives and theirassociated computer-readable storage media provide non-volatile storageof computer-readable instructions, data structures, program modules, andother data for the computer system 2200. Although this illustrativeexample includes a hard disk, a removable magnetic disk 2233, and aremovable optical disk 2243, other types of computer-readable storagemedia which can store data that is accessible by a computer such asmagnetic cassettes, Flash memory cards, digital video disks, datacartridges, random access memories (RAMs), read only memories (ROMs),and the like may also be used in some applications of the presentdynamic 5G network slicing to maximize spectrum utilization. Inaddition, as used herein, the term computer-readable storage mediaincludes one or more instances of a media type (e.g., one or moremagnetic disks, one or more CDs, etc.). For purposes of thisspecification and the claims, the phrase “computer-readable storagemedia” and variations thereof, are intended to cover non-transitoryembodiments, and does not include waves, signals, and/or othertransitory and/or intangible communication media.

A number of program modules may be stored on the hard disk, magneticdisk 2233, optical disk 2243, ROM 2217, or RAM 2221, including anoperating system 2255, one or more application programs 2257, otherprogram modules 2260, and program data 2263. A user may enter commandsand information into the computer system 2200 through input devices suchas a keyboard 2266 and pointing device 2268 such as a mouse. Other inputdevices (not shown) may include a microphone, joystick, game pad,satellite dish, scanner, trackball, touchpad, touchscreen,touch-sensitive device, voice-command module or device, user motion oruser gesture capture device, or the like. These and other input devicesare often connected to the processor 2205 through a serial portinterface 2271 that is coupled to the system bus 2214, but may beconnected by other interfaces, such as a parallel port, game port, oruniversal serial bus (USB). A monitor 2273 or other type of displaydevice is also connected to the system bus 2214 via an interface, suchas a video adapter 2275. In addition to the monitor 2273, personalcomputers typically include other peripheral output devices (not shown),such as speakers and printers. The illustrative example shown in FIG. 22also includes a host adapter 2278, a Small Computer System Interface(SCSI) bus 2283, and an external storage device 2276 connected to theSCSI bus 2283.

The computer system 2200 is operable in a networked environment usinglogical connections to one or more remote computers, such as a remotecomputer 2288. The remote computer 2288 may be selected as anotherpersonal computer, a server, a router, a network PC, a peer device, orother common network node, and typically includes many or all of theelements described above relative to the computer system 2200, althoughonly a single representative remote memory/storage device 2290 is shownin FIG. 22 . The logical connections depicted in FIG. 22 include a localarea network (LAN) 2293 and a wide area network (WAN) 2295. Suchnetworking environments are often deployed, for example, in offices,enterprise-wide computer networks, intranets, and the Internet.

When used in a LAN networking environment, the computer system 2200 isconnected to the local area network 2293 through a network interface oradapter 2296. When used in a WAN networking environment, the computersystem 2200 typically includes a broadband modem 2298, network gateway,or other means for establishing communications over the wide areanetwork 2295, such as the Internet. The broadband modem 2298, which maybe internal or external, is connected to the system bus 2214 via aserial port interface 2271. In a networked environment, program modulesrelated to the computer system 2200, or portions thereof, may be storedin the remote memory storage device 2290. It is noted that the networkconnections shown in FIG. 22 are illustrative and other means ofestablishing a communications link between the computers may be useddepending on the specific requirements of an application of the presentdynamic 5G network slicing to maximize spectrum utilization.

Various exemplary embodiments of the present dynamic 5G network slicingto maximize spectrum utilization are now presented by way ofillustration and not as an exhaustive list of all embodiments. Anexample includes a computer-implemented method for scheduling datatransmission on an air interface of a 5G (fifth generation) network, inwhich the air interface is established between a radio unit (RU) and aplurality of user equipment (UE), and in which the 5G network comprisesa plurality of slices, the computer-implemented method comprising:allocating physical radio resources in the air interface, the physicalradio resources being partitioned in segments comprising respectivesubcarriers and time slots, in which the subcarriers use dimensions ofbandwidth and the time slots use dimensions of time; collecting channelstate information (CSI) data from the UE that represents conditions fordata transmission over the air interface between the RU and UE; usingthe collected CSI data to predict CSI that is applicable for subcarriersat future time slots that are available for handling the datatransmissions; and using the predicted CSI to select subcarriers andtime slots for data transmissions over the air interface between the RUand the UE for a slice of the 5G network, wherein criteria for theselection comprise maximization of throughput for the slice over the airinterface per bandwidth utilized, in which the throughput comprises datatransmitted per unit time.

In another example, the physical radio resources are partitioned intosegments defined by one or more numerologies, each numerology beingdifferent and expressing at least one of subcarrier spacing, cyclicprefix type, OFDM (orthogonal frequency-division multiplexing) symbolcount, radio frame structure, or time slot length. In another example,the computer-implemented method further includes scheduling data fortransmission using the selected subcarriers and time slots. In anotherexample, the computer-implemented method further includes transmittingdata based on the scheduling. In another example, the subcarriers areselected from one or more different numerologies, each numerologydescribing at least one of subcarrier spacing, time slot length, orframe structure. In another example, the computer-implemented method isperformed, at least in part, in a near-real-time radio access networkintelligent controller (near-RT RIC) as described by the O-RAN Alliance.In another example, the computer-implemented method further includesperforming the scheduling using a MAC (Medium Access Control) layercomponent disposed in a distributed unit (DU) of a 5G RAN (radio accessnetwork). In another example, the MAC layer in the DU interoperates witha PHY (physical) layer functionality disposed in the RU.

A further example includes one or more hardware-based non-transitorycomputer-readable memory devices storing computer-executableinstructions which, upon execution by one or more processor disposed ina computing device, cause the computing device to: provide a channelstate information (CSI) prediction model to predict CSI for userequipment (UE) connecting to a 5G (fifth generation) network over an airinterface that is established between the UE and a radio unit (RU), inwhich the 5G network comprises a plurality of slices; use the predictedCSI to identify physical radio resources to allocate to a slice amongthe plurality to transmit data from the RU to the UE, in which thephysical radio resources comprise a combination of a radio channelsubcarrier selected from among a plurality of different subcarriers anda time slot in radio frames selected from among a plurality of differenttime slots; and schedule data transmissions for the slice using theidentified physical radio resources based on the predicted CSI tomaximize throughput for the slice over the air interface.

In another example, the plurality of subcarriers comprise radio spectrumbandwidth, and the data transmissions are further scheduled to maximizethroughput per bandwidth that is utilized by the selected subcarriers.In another example, the CSI prediction model comprises a predictivepropagation model. In another example, the predictive propagation modeluses reinforcement learning that considers CSI data from one or moreonline or offline sources. In another example, the online sources of CSIdata comprise CSI feedback from a population of UE that is engaged incurrent communications sessions on the 5G network over the airinterface, the CSI feedback reporting dynamic channel conditions betweenthe RU and the UE.

A further example includes a computing device, comprising: at least oneprocessor; and at least one hardware-based non-transitorycomputer-readable storage device having computer-executable instructionsstored thereon which, when executed by the least one processor, causethe computing device to dynamically operate a radio access network (RAN)in a 5G (fifth generation) network by collecting channel stateinformation (CSI) data in response to changing conditions on the 5Gnetwork applicable to current data transmissions over slices of an airinterface established between user equipment (UE) and a radio unit (RU)in the 5G network, in which the 5G network comprises a plurality ofslices that are respectively associated with the slices of the airinterface; use the collected CSI data to generate predicted CSI forfuture data transmissions over the slices of the air interface; select acombination of subcarrier and time slot for each of the future datatransmissions using the predicted CSI; and schedule the future datatransmissions on the slices of the air interface using the selectedcombination of subcarrier and time slot.

In another example, the computer-executable instructions, when executedby the least at one processor, further cause the computing device tomaximize total throughput across the slices of the air interface. Inanother example, the computer-executable instructions, when executed bythe at least one processor, further cause the computing device tomaximize utilization of a radio spectrum, in which subcarriers aredistributed in a frequency domain across the radio spectrum. In anotherexample, the computer-executable instructions, when executed by the atleast one processor, further cause the computing device to transmit orreceive data transmissions based on the scheduled future datatransmission over a respective air interface downlink or an airinterface uplink. In another example, a time slot length in a timedomain is inversely proportional to subcarrier spacing in a frequencydomain. In another example, a near-real-time radio access networkintelligent controller (near-RT RIC) interoperates with a MAC (MediumAccess Control) layer component to control radio resource allocationamong slices of the air interface, the radio resources being expressedby subcarrier and time slot. In another example, the radio resources arepartitioned into segments being defined by a numerology, the numerologyreferring to values of physical transmission parameters defining the airinterface.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed:
 1. A computer-implemented method for scheduling datatransmission on an air interface of a 5G (fifth generation) network, inwhich the air interface is established between a radio unit (RU) and aplurality of user equipment (UE), and in which the 5G network comprisesa plurality of slices, the computer-implemented method comprising:allocating physical radio resources in the air interface, the physicalradio resources being partitioned in segments comprising respectivesubcarriers and time slots, in which the subcarriers use dimensions ofbandwidth and the time slots use dimensions of time; collecting channelstate information (CSI) data from the UE that represents conditions fordata transmission over the air interface between the RU and UE; usingthe collected CSI data to predict CSI that is applicable for subcarriersat future time slots that are available for handling the datatransmissions; and using the predicted CSI to select subcarriers andtime slots for data transmissions over the air interface between the RUand the UE for a slice of the 5G network, wherein criteria for theselection comprise maximization of throughput for the slice over the airinterface per bandwidth utilized, in which the throughput comprises datatransmitted per unit time.
 2. The computer-implemented method of claim 1in which the physical radio resources are partitioned into segmentsdefined by one or more numerologies, each numerology being different andexpressing at least one of subcarrier spacing, cyclic prefix type, OFDM(orthogonal frequency-division multiplexing) symbol count, radio framestructure, or time slot length.
 3. The computer-implemented method ofclaim 1 further including scheduling data for transmission using theselected subcarriers and time slots.
 4. The computer-implemented methodof claim 3 further including transmitting data based on the scheduling.5. The computer-implemented method of claim 1 in which the subcarriersare selected from one or more different numerologies, each numerologydescribing at least one of subcarrier spacing, time slot length, orframe structure.
 6. The computer-implemented method of claim 1 in whichthe computer-implemented method is performed, at least in part, in anear-real-time radio access network intelligent controller (near-RT RIC)as described by the O-RAN Alliance.
 7. The computer-implemented methodof claim 1 further including performing the scheduling using a MAC(Medium Access Control) layer component disposed in a distributed unit(DU) of a 5G RAN (radio access network).
 8. The computer-implementedmethod of claim 7 in which the MAC layer in the DU interoperates with aPHY (physical) layer functionality disposed in the RU.
 9. One or morehardware-based non-transitory computer-readable memory devices storingcomputer-executable instructions which, upon execution by one or moreprocessor disposed in a computing device, cause the computing device to:provide a channel state information (CSI) prediction model to predictCSI for user equipment (UE) connecting to a 5G (fifth generation)network over an air interface that is established between the UE and aradio unit (RU), in which the 5G network comprises a plurality ofslices; use the predicted CSI to identify physical radio resources toallocate to a slice among the plurality to transmit data from the RU tothe UE, in which the physical radio resources comprise a combination ofa radio channel subcarrier selected from among a plurality of differentsubcarriers and a time slot in radio frames selected from among aplurality of different time slots; and schedule data transmissions forthe slice using the identified physical radio resources based on thepredicted CSI to maximize throughput for the slice over the airinterface.
 10. The one or more hardware-based non-transitorycomputer-readable memory devices of claim 9 in which the plurality ofsubcarriers comprise radio spectrum bandwidth, and the datatransmissions are further scheduled to maximize throughput per bandwidththat is utilized by the selected subcarriers.
 11. The one or morehardware-based non-transitory computer-readable memory devices of claim9 in which the CSI prediction model comprises a predictive propagationmodel.
 12. The one or more hardware-based non-transitorycomputer-readable memory devices of claim 11 in which the predictivepropagation model uses reinforcement learning that considers CSI datafrom one or more online or offline sources.
 13. The one or morehardware-based non-transitory computer-readable memory devices of claim12 in which the online sources of CSI data comprise CSI feedback from apopulation of UE that is engaged in current communications sessions onthe 5G network over the air interface, the CSI feedback reportingdynamic channel conditions between the RU and the UE.
 14. A computingdevice, comprising: at least one processor; and at least onehardware-based non-transitory computer-readable storage device havingcomputer-executable instructions stored thereon which, when executed bythe least one processor, cause the computing device to dynamicallyoperate a radio access network (RAN) in a 5G (fifth generation) networkby collecting channel state information (CSI) data in response tochanging conditions on the 5G network applicable to current datatransmissions over slices of an air interface established between userequipment (UE) and a radio unit (RU) in the 5G network, in which the 5Gnetwork comprises a plurality of slices that are respectively associatedwith the slices of the air interface; use the collected CSI data togenerate predicted CSI for future data transmissions over the slices ofthe air interface; select a combination of subcarrier and time slot foreach of the future data transmissions using the predicted CSI; andschedule the future data transmissions on the slices of the airinterface using the selected combination of subcarrier and time slot.15. The computing device of claim 14 in which the computer-executableinstructions, when executed by the least at one processor, further causethe computing device to maximize total throughput across the slices ofthe air interface.
 16. The computing device of claim 14 in which thecomputer-executable instructions, when executed by the at least oneprocessor, further cause the computing device to maximize utilization ofa radio spectrum, in which subcarriers are distributed in a frequencydomain across the radio spectrum.
 17. The computing device of claim 14in which the computer-executable instructions, when executed by the atleast one processor, further cause the computing device to transmit orreceive data transmissions based on the scheduled future datatransmission over a respective air interface downlink or an airinterface uplink.
 18. The computing device of claim 14 in which a timeslot length in a time domain is inversely proportional to subcarrierspacing in a frequency domain.
 19. The computing device of claim 14 inwhich a near-real-time radio access network intelligent controller(near-RT RIC) interoperates with a MAC (Medium Access Control) layercomponent to control radio resource allocation among slices of the airinterface, the radio resources being expressed by subcarrier and timeslot.
 20. The computing device of claim 19 in which the radio resourcesare partitioned into segments being defined by a numerology, thenumerology referring to values of physical transmission parametersdefining the air interface.